# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import unittest

import numpy as np

from extensions.back.ShufflenetReLUReorder import ShufflenetReLUReorder
from mo.utils.ir_engine.compare_graphs import compare_graphs
from unit_tests.utils.graph import build_graph

# The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the
# dictionary with node attributes.
nodes_attributes = {
    'placeholder_1': {'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
    'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
    # ReLU
    'relu_1': {'type': 'ReLU', 'kind': 'op', 'op': 'ReLU'},
    'relu_1_data': {'value': None, 'shape': None, 'kind': 'data'},
    # Reshape layers
    'reshape_1': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
    'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
    'reshape_2': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
    'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
    'reshape_3': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
    'reshape_3_data': {'value': None, 'shape': None, 'kind': 'data'},
    # Transpose layer
    'transpose_1': {'type': 'Transpose', 'kind': 'op', 'op': 'Transpose'},
    'transpose_1_data': {'value': None, 'shape': None, 'kind': 'data'},
    # Conv layer
    'conv_1': {'type': 'Convolution', 'kind': 'op', 'op': 'Conv2d'},
    'conv_1_data': {'value': None, 'shape': None, 'kind': 'data'},
}


class ShufflenetReLUReorderTests(unittest.TestCase):
    def test_1(self):
        graph = build_graph(nodes_attributes,
                            [('placeholder_1', 'placeholder_1_data'),
                             ('placeholder_1_data', 'relu_1'),
                             ('relu_1', 'relu_1_data'),
                             ('relu_1_data', 'reshape_1'),
                             ('reshape_1', 'reshape_1_data'),
                             ('reshape_1_data', 'transpose_1'),
                             ('transpose_1', 'transpose_1_data'),
                             ('transpose_1_data', 'reshape_2'),
                             ('reshape_2', 'reshape_2_data'),
                             ('reshape_2_data', 'conv_1'),
                             ('conv_1', 'conv_1_data')
                             ],
                            {'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
                             'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
                             'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
                             'transpose_1': {'order': np.array([0, 1, 3, 2])},
                             'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
                             'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
                             'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
                             'conv_1': {'pad': np.array([1, 1])}
                             })
        graph.graph['layout'] = 'NHWC'

        graph_ref = build_graph(nodes_attributes,
                                [('placeholder_1', 'placeholder_1_data'),
                                 ('placeholder_1_data', 'reshape_1'),
                                 ('reshape_1', 'reshape_1_data'),
                                 ('reshape_1_data', 'transpose_1'),
                                 ('transpose_1', 'transpose_1_data'),
                                 ('transpose_1_data', 'reshape_2'),
                                 ('reshape_2', 'reshape_2_data'),
                                 ('reshape_2_data', 'relu_1'),
                                 ('relu_1', 'relu_1_data'),
                                 ('relu_1_data', 'conv_1'),
                                 ('conv_1', 'conv_1_data')
                                 ],
                                {'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
                                 'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
                                 'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
                                 'transpose_1': {'order': np.array([0, 1, 3, 2])},
                                 'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
                                 'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
                                 'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
                                 })

        pattern = ShufflenetReLUReorder()
        pattern.find_and_replace_pattern(graph)

        (flag, resp) = compare_graphs(graph, graph_ref, 'conv_1_data', check_op_attrs=True)
        self.assertTrue(flag, resp)

    def test_2_neg(self):
        graph = build_graph(nodes_attributes,
                            [('placeholder_1', 'placeholder_1_data'),
                             ('placeholder_1_data', 'reshape_1'),
                             ('reshape_1', 'reshape_1_data'),
                             ('reshape_1_data', 'transpose_1'),
                             ('transpose_1', 'transpose_1_data'),
                             ('transpose_1_data', 'reshape_2'),
                             ('reshape_2', 'reshape_2_data'),
                             ('reshape_2_data', 'conv_1'),
                             ('conv_1', 'conv_1_data')
                             ],
                            {'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
                             'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
                             'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
                             'transpose_1': {'order': np.array([0, 1, 3, 2])},
                             'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
                             'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
                             'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
                             })
        graph.graph['layout'] = 'NHWC'

        graph_ref = build_graph(nodes_attributes,
                                [('placeholder_1', 'placeholder_1_data'),
                                 ('placeholder_1_data', 'reshape_1'),
                                 ('reshape_1', 'reshape_1_data'),
                                 ('reshape_1_data', 'transpose_1'),
                                 ('transpose_1', 'transpose_1_data'),
                                 ('transpose_1_data', 'reshape_2'),
                                 ('reshape_2', 'reshape_2_data'),
                                 ('reshape_2_data', 'conv_1'),
                                 ('conv_1', 'conv_1_data')
                                 ],
                                {'placeholder_1_data': {'shape': np.array([1, 227, 227, 112])},
                                 'relu_1_data': {'shape': np.array([1, 227, 227, 112])},
                                 'reshape_1_data': {'shape': np.array([227, 227, 4, 28])},
                                 'transpose_1': {'order': np.array([0, 1, 3, 2])},
                                 'transpose_1_data': {'shape': np.array([227, 227, 28, 4])},
                                 'reshape_2_data': {'shape': np.array([1, 227, 227, 112])},
                                 'conv_1_data': {'shape': np.array([1, 227, 227, 112])},
                                 })

        pattern = ShufflenetReLUReorder()
        pattern.find_and_replace_pattern(graph)

        (flag, resp) = compare_graphs(graph, graph_ref, 'conv_1_data', check_op_attrs=True)
        self.assertTrue(flag, resp)
